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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data_4 when this report was generated.


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        If you use plots from MultiReport in a publication or presentation, please cite:

        MultiReport: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiReport

        This report was generated using MultiReport, which was developed based on MultiQC.

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from Quartet Data Portal App into a single report.

        Report generated on 2022-11-10, 20:38 based on data in Quartet Data Portal.


        Data Generation Information

        The basic information about the sequencing data.

        Lab
        BGI
        Sequencing Platform
        DipSEQ
        Sequencing Method
        WES
        Library Protocol
        PCR
        Library Kit
        MGIEasy Universal DNA Library Prep Set
        Read Mode and Length
        PE100bp
        Date
        2020/7/16

        Assessment Summary

        Evaluation metrics

        The performance of the submitted data will be graded as Bad, Fair, Good, or Great based on the ranking by comparing the total score with the historical datasets.
        The total score is an F0.5-measure of the SNV score and the INDEL score, which are the mean values of Precision, Recall, and MCR, respectively.

        Evaluation metrics:

        • The total score is an F0.5-measure of the SNV score and the INDEL score, which are the mean values of Precision, Recall, and MCR, respectively. The total score and the results of the evaluation metrics are presented in the table below.
        • For better comparison and presentation, the total score was scaled to the interval [1, 10], with the worst dataset being 1 and the best dataset scoring 10.

        Four levels of performance:

        Based on the scaled total score, the submitted data will be ranked together with all Quartet historical datasets. The higher the score, the higher the ranking. After this, the performance levels will be assigned based on their ranking ranges.

        • Bad - the bottom 20%.
        • Fair - between bottom 20% and median 50%.
        • Good - between median 50% and top 20%.
        • Great - the top 20%.
        8.48
        Bad
        Fair
        Good
        Great
        1 9.09 9.34 9.51 10
        Showing 7/7 rows and 4/4 columns.
        Quality MetricsValueHistorical ValueRankPerformance
        Precision (SNV)0.9950.981 ± 0.07036 / 81Good
        Precision (INDEL)0.9490.883 ± 0.0877 / 81Great
        Recall (SNV)0.8900.978 ± 0.04879 / 81Bad
        Recall (INDEL)0.6560.911 ± 0.07980 / 81Bad
        Mendelian Concordance Rate (SNV)0.9580.946 ± 0.09360 / 81Fair
        Mendelian Concordance Rate (INDEL)0.8340.793 ± 0.09123 / 81Good
        Total Score0.8960.929 ± 0.06971 / 81Bad

        Performance of SNV and INDEL

        Due to the apparent differences between SNV and INDEL, the performance of the two types of small variants of the evaluated data compared to the Quartet historical batches is shown separately in this section. Each data point represents a set of Quartet samples, i.e., one each of D5, D6, F7, and M8.

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        Historical scores

        Scores of evaluation metrics for the current batch and all historical batches assessed. The name of your data is Queried_Data.

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        Pre-alignment Quality Control

        Pre-alignment QC is an report module to show the data quality before alignment.

        Summary metrics

        Showing 8/8 rows and 12/12 columns.
        Sample% Dups% GCM Seqs% Human% EColi% Adapter% Vector% rRNA% Virus% Yeast% Mitoch% No hits
        R21045930-pool-1-dVF1-D5-1_combined_test_qdp_wes_rly_20221109_2_LCL5_R1
        15.16
        52.00
        13.45
        99.69
        0.00
        3.54
        3.88
        0.02
        0.24
        0.11
        0.02
        0.28
        R21045930-pool-1-dVF1-D5-1_combined_test_qdp_wes_rly_20221109_2_LCL5_R2
        14.84
        52.00
        13.45
        99.37
        0.00
        2.77
        3.21
        0.02
        0.30
        0.13
        0.02
        0.57
        R21045930-pool-1-dVF1-D6-1_combined_test_qdp_wes_rly_20221109_2_LCL6_R1
        15.77
        52.00
        14.17
        99.69
        0.00
        3.05
        3.38
        0.02
        0.23
        0.11
        0.01
        0.28
        R21045930-pool-1-dVF1-D6-1_combined_test_qdp_wes_rly_20221109_2_LCL6_R2
        15.46
        52.00
        14.17
        99.37
        0.00
        2.23
        3.19
        0.02
        0.31
        0.14
        0.01
        0.56
        R21045930-pool-1-dVF1-F7-1_combined_test_qdp_wes_rly_20221109_2_LCL7_R1
        19.07
        51.00
        19.05
        99.71
        0.00
        2.29
        2.57
        0.02
        0.22
        0.11
        0.02
        0.27
        R21045930-pool-1-dVF1-F7-1_combined_test_qdp_wes_rly_20221109_2_LCL7_R2
        18.68
        51.00
        19.05
        99.30
        0.00
        1.98
        2.53
        0.02
        0.30
        0.14
        0.02
        0.64
        R21045933-pool-4-dVF1-M8-1_combined_test_qdp_wes_rly_20221109_2_LCL8_R1
        19.91
        51.00
        23.27
        99.69
        0.00
        2.68
        3.28
        0.02
        0.23
        0.11
        0.03
        0.27
        R21045933-pool-4-dVF1-M8-1_combined_test_qdp_wes_rly_20221109_2_LCL8_R2
        19.65
        51.00
        23.27
        99.40
        0.00
        2.26
        3.07
        0.02
        0.30
        0.13
        0.03
        0.53

        Sequence quality histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

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        Per sequence GC content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

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        Post-alignment Quality Control

        Post-alignment QC is an report module to show the data quality after alignment.

        Summary metrics

        Showing 4/4 rows and 11/11 columns.
        Sample% Aligned% MismatchIns. sizeMedian cov≥ 1X≥ 5X≥ 10X≥ 20X≥ 30XFold-80% On-target base
        R21045930-pool-1-dVF1-D5-1_combined_test_qdp_wes_rly_20221109_2_LCL5
        99.96
        0.19
        207.00
        32.00X
        93.38%
        92.02%
        90.71%
        80.37%
        56.27%
        1.53
        49.00%
        R21045930-pool-1-dVF1-D6-1_combined_test_qdp_wes_rly_20221109_2_LCL6
        99.96
        0.19
        212.00
        35.00X
        93.33%
        92.11%
        91.10%
        83.93%
        63.47%
        1.44
        49.00%
        R21045930-pool-1-dVF1-F7-1_combined_test_qdp_wes_rly_20221109_2_LCL7
        99.95
        0.19
        222.00
        46.00X
        93.56%
        92.52%
        91.79%
        88.73%
        79.86%
        1.44
        49.00%
        R21045933-pool-4-dVF1-M8-1_combined_test_qdp_wes_rly_20221109_2_LCL8
        99.96
        0.18
        210.00
        53.00X
        93.79%
        92.46%
        91.84%
        90.33%
        85.68%
        1.38
        48.00%

        Coverage histogram

        Distribution of the number of locations in the reference genome with a given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

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        Cumulative genome coverage

        Percentage of the reference genome with at least the given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

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        Insert size histogram

        Distribution of estimated insert sizes of mapped reads.

        To overcome limitations in the length of DNA or RNA sequencing reads, many sequencing instruments can produce two or more shorter reads from one longer fragment in which the relative position of reads is approximately known, such as paired-end or mate-pair reads (Mardis 2013). Such techniques can extend the reach of sequencing technology, allowing for more accurate placement of reads (Reinert et al. 2015) and better resolution of repeat regions (Reinert et al. 2015), as well as detection of structural variation (Alkan et al. 2011) and chimeric transcripts (Maher et al. 2009).

        All these methods assume that the approximate size of an insert is known. (Insert size can be defined as the length in bases of a sequenced DNA or RNA fragment, excluding technical sequences such as adapters, which are typically removed before alignment.) This plot allows for that assumption to be assessed. With the set of mapped fragments for a given sample, QualiMap groups the fragments by insert size, then plots the frequency of mapped fragments (y-axis) over a range of insert sizes (x-axis). In an ideal case, the distribution of fragment sizes for a sequencing library would culminate in a single peak indicating average insert size, with a narrow spread indicating highly consistent fragment lengths.

        QualiMap calculates insert sizes as follows: for each fragment in which every read mapped successfully to the same reference sequence, it extracts the insert size from the TLEN field of the leftmost read (see the Qualimap 2 documentation), where the TLEN (or 'observed Template LENgth') field contains 'the number of bases from the leftmost mapped base to the rightmost mapped base' (SAM format specification). Note that because it is defined in terms of alignment to a reference sequence, the value of the TLEN field may differ from the insert size due to factors such as alignment clipping, alignment errors, or structural variation or splicing in a gap between reads from the same fragment.

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        GC content distribution

        Each solid line represents the distribution of GC content of mapped reads for a given sample.

        GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).

        QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).

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        Variant Calling Quality Control

        Variant calling QC is an report module to show quality assessment of the variant calling.

        Details based on reference datasets

        Showing 4/4 rows and 16/16 columns.
        SampleSNV numSNV querySNV TPSNV FPSNV FNSNV precisionSNV recallSNV F1INDEL numINDEL queryINDEL TPINDEL FPINDEL FNINDEL precisionINDEL recallINDEL F1
        test_qdp_wes_rly_20221109_2.R21045930-pool-1-dVF1-D5-1_combined_test_qdp_wes_rly_20221109_2_LCL5_hc.chrom.bed2496522694225521422869
        0.9937
        0.8871
        0.9374
        90767162546344
        0.9314
        0.6439
        0.7614
        test_qdp_wes_rly_20221109_2.R21045930-pool-1-dVF1-D6-1_combined_test_qdp_wes_rly_20221109_2_LCL6_hc.chrom.bed249842269022591992830
        0.9956
        0.8887
        0.9391
        90165763126338
        0.9604
        0.6501
        0.7754
        test_qdp_wes_rly_20221109_2.R21045930-pool-1-dVF1-F7-1_combined_test_qdp_wes_rly_20221109_2_LCL7_hc.chrom.bed2534822847227351122709
        0.9951
        0.8935
        0.9416
        93365462034328
        0.9480
        0.6522
        0.7727
        test_qdp_wes_rly_20221109_2.R21045933-pool-4-dVF1-M8-1_combined_test_qdp_wes_rly_20221109_2_LCL8_hc.chrom.bed2519622873227431302747
        0.9943
        0.8922
        0.9405
        95269866632316
        0.9542
        0.6762
        0.7915

        Details based on Quartet genetic built-in truth

        Each row represents a set of Quartet samples, i.e. one each of D5, D6, F7 and M8. When multiple sets of technical replicates are measured, the performance of each set will be represented by row.

        Showing 1/1 rows and 4/4 columns.
        SNV Detected VariantsSNV Mendelian Consistent VariantsINDEL Detected VariantsINDEL Mendelian Consistent Variants
        Queried_Data_Set1338683245613631137

        Supplementary

        The additional information about this quality assessment report.

        Methods

        1. Tested call sets were compared with benchmark small variants using hap.py (https://github.com/Illumina/hap.py). Precision is the fraction of called variants in the test dataset that are true, and recall is the fraction of true variants are called in the test dataset. True Positives (TP) are true variants detected in the test dataset. False Negatives (FN) are variants in the reference dataset failed to be detected in the test dataset. False Positive (FP) are variants called in the test dataset but not included in the reference dataset. Precision and recall are defined as below:

      • Precision = TP / (TP+FP)
      • Recall=TP/(TP+FN)
      • F1=(2×Precision×Recall)/(Precision+Recall)
      • 2. Mendelian concordance rate (MCR) is the number of variants following Mendelian inheritance laws divided by the total number of variants called among the four Quartet samples. Mendelian concordant variants are the variants shared by the twins (D5 and D6) and following Mendelian inheritance laws with parents (Father: F7 and Mother M8). Mendelian analysis was performed using VBT (https://github.com/sbg/VBT-TrioAnalysis). When calculating Mendelian concordance rate of small variants, variants on large deletions were not included, because VBT takes these variants as Mendelian violations.

        3. Total score = (1+0.52) x SNV_score x INDEL_score / (0.52 x SNV_score + INDEL_score). SNV_score and INDEL_score are obtained by calculating the mean values of Precision, Recall, and MCR, respectively.

        Pipeline

        We accepted fastq files, and used Sentieon Genomics to call germline small variants. [1] The quality control consists of pre-alignment, post-alignment and variants calling quality control. Pre-alignment quality control focuses on raw fastq files and helps to determine systematic bias and library issue, such as sequencing quality issue, high GC or AT, PCR bias, adapter contaminant, cross species contamination. FastQC [2] and FastQ Screen [3] are used to evaluate raw reads quality. Post-alignment quality control focuses on bam files and helps to measure library performance and sample variance, such as sequencing error rate, sequencing depth and coverage consistency. Qualimap [4] is used to evaluate quality of bam files. Variants calling quality control is to examine accuracy of detected variants based on reference datasets, and estimate potential sequence errors by reproducibility of monozygotic twin daughters and mendelian concordant ratio of Quartet family.

        Software

        Sentieon Genomics (FASTQ to VCF)
        v2019.11.28
        FastQC
        v0.11.8
        FastQ Screen
        v0.12.0
        Qualimap
        v2.0.0
        hap.py
        v0.3.7
        VBT (mendelian analysis)
        v1.1

        Contact us

        Fudan University Pharmacogenomics Research Center
      • Project manager: Quartet Team
      • Email: quartet@fudan.edu.cn
      • Disclaimer

        This quality control report is only for this specific test data set and doesn’t represent an evaluation of the business level of the sequencing company. This report is only used for scientific research, not for clinical or commercial use. We don’t bear any economic and legal liabilities for any benefits or losses (direct or indirect) from using the results of this report.